A third objective, trajectory balance (TB), was proposed in [1]} to address the problem of slow temporal credit assignment with the FM and DB objectives.
The TB objective propagates learning signals over entire episodes, while the temporal difference-like objectives (FM and DB) make updates local to states or actions. It has been hypothesized by [1]} that the improved credit assignment with TB comes at the cost of higher gradient variance, analogous to the bias-variance tradeoff seen in temporal difference learning (TD\((n)\)  or TD\((\lambda )\) ) with different eligibility trace schemes [3]}, [4]}, [5]}, [6]}. This hypothesis is one of the starting points for the present paper.
